The success of software projects depends on complex decision making (e.g., which tasks should a developer do first, who should perform this task, is the software of high quality, is a software system reliable and resilient enough to deploy, etc.). Bad decisions cost money (and reputation) so we need better tools for making better decisions. This article discusses the "why" and "how" of explainable and actionable software analytics. For the task of reducing the risk of software defects, we show initial results from a successful case study that offers more actionable software analytics. Also, we present an interactive tutorial on the subject of Explainable AI tools for SE in our Software Analytics Cookbook (https://xai4se.github.io/book/), and we discuss some open questions that need to be addressed.

中文翻译：

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可行的分析：别再告诉我它是什么了；请告诉我该怎么做


软件项目的成功取决于复杂的决策（例如，开发人员应该首先执行哪些任务、谁应该执行该任务、软件是否高质量、软件系统是否可靠且有足够的弹性来部署等）。错误的决策会耗费金钱（和声誉），因此我们需要更好的工具来做出更好的决策。本文讨论可解释且可操作的软件分析的“原因”和“如何”。为了降低软件缺陷风险的任务，我们展示了成功案例研究的初步结果，该案例研究提供了更具可操作性的软件分析。此外，我们在 Software Analytics Cookbook (https://xai4se.github.io/book/) 中提供了关于 SE 的可解释 AI 工具主题的交互式教程，并讨论了一些需要解决的开放性问题。